Knowledge By the end of the course, students will have acquired knowledge of: * Python programming for optical network analysis and control * State-of-the-art optical transceivers for modern optical communication systems * Fundamentals of optical fiber propagation and modeling of physical-layer impairments * Optical amplifiers and passive components used in photonic networks * WDM spectral usage, fixed-grid and flex-grid architectures, and related standards * ROADMs and the general structure of optical network nodes * Open network management concepts, including YANG data models and NETCONF * Basic concepts of machine learning applied to optical network control * Fundamentals of quantum key distribution and its integration in optical fiber networks Abilities By the end of the course, students will be able to: * Develop Python code within a GitHub-based workflow * Emulate the optical layer of photonic networks through simplified digital-twin models * Perform routing, spectrum assignment, and wavelength assignment in optical networks * Analyze multilayer orchestration problems below and across the IP layer * Carry out physical-layer-aware network analysis, design, and optimization * Use a network digital twin to evaluate the impact of propagation impairments, component constraints, and network configurations * Apply open and programmable network-control concepts to simplified optical networking scenarios

The course requires basic knowledge of signal analysis and digital transmission. A general understanding of the Internet architecture is useful, but not mandatory. Basic computer programming skills are also required, as students will develop simple software modules during the course. To support students with different backgrounds, introductory recap lectures on the fundamentals of optical communications and data networks will be provided. These lectures are intended to ensure that all students can profitably follow the subsequent topics, regardless of their previous preparation.

* Introduction to open optical networks and software-defined optical networking * History and basic concepts of optical communications and optical transport networks * Optical transmission systems: transceivers, modulation formats, OSNR, BER, and coherent detection * Optical fiber propagation: attenuation, dispersion, polarization effects, Kerr nonlinearity, and nonlinear interference * WDM systems: spectral grids, fixed-grid and flex-grid operation, wavelength/spectrum usage and standards * Optical line systems: amplifiers, passive components, ROADMs, node architectures, and disaggregated network models * Optical network digital twin: physical-layer modeling, QoT estimation, GSNR abstraction, telemetry, APIs, and network emulation * Network design and planning: statistical network assessment, traffic matrices, lightpath feasibility, and multilayer optimization * Routing and wavelength/spectrum assignment for physical-layer-aware optical networks * Open control and automation: SDN, YANG/NETCONF, APIs, Python/GitHub laboratories, and AI-assisted network control * Optical ISAC: integrated sensing and communication over optical fiber networks, including monitoring, sensing-assisted control, and infrastructure awareness

The course assessment also offers students the opportunity to continue their work through Master’s thesis projects carried out within the Telecom Infra Project and IOWN Global Forum frameworks, in collaboration with vendors and network operators. Possible thesis activities may include the development of open APIs abstracting physical-layer functionalities within open network operating systems, such as ONOS, and their experimental validation on the open network infrastructure available in the Open PLANET Lab at Politecnico di Torino. The OON students will possibly attend the Subsea Wave (https://www.suboptic.org/wave) event by NOKIA in Vimercate, early November 2026

The course is organized as an integrated path combining theoretical lectures, application-oriented examples, software laboratories, and interactions with the optical networking ecosystem. The structure is designed to progressively move from the physical foundations of optical transmission to open and programmable optical network control. The first part of the course introduces the context and motivation of open optical networks. It reviews the evolution of optical communications and optical transport networks, highlighting the role of optical fiber as the physical infrastructure supporting modern data networking. Basic concepts such as optical line systems, transceivers, WDM transmission, optical nodes, ROADMs, and transport network architectures are introduced. The second part focuses on the physical layer of optical networks. Students will study optical transmission impairments and their impact on quality of transmission, including attenuation, chromatic dispersion, polarization effects, amplifier noise, Kerr nonlinearities, and nonlinear interference. These concepts are then used to build simplified but effective models of optical propagation and transmission performance, based on OSNR, SNR, GSNR, BER, and QoT estimation. The third part addresses the abstraction of the optical layer and the construction of an optical network digital twin. Network elements and subsystems are progressively modeled as software objects, enabling the emulation of the physical layer and the prediction of lightpath feasibility. The digital twin is used as a basis for network design, planning, routing and wavelength or spectrum assignment, multilayer optimization, and physical-layer-aware control. The fourth part focuses on open control and automation. Students will be introduced to software-defined networking concepts applied below the IP layer, open APIs, common data models, YANG, NETCONF, network operating systems, and programmable control procedures. Particular attention will be given to open and disaggregated optical network architectures, where transceivers, ROADMs, optical line systems, controllers, and telemetry sources can be represented and managed through interoperable software interfaces. A dedicated part of the course is devoted to AI-assisted and data-driven optical networking. Students will be introduced to the use of machine learning and cognitive control techniques for optical network monitoring, modeling, optimization, and autonomous operation. Optical ISAC will also be addressed as an emerging topic, with emphasis on integrated sensing and communication over optical fiber infrastructures, network monitoring, sensing-assisted control, and infrastructure awareness. The laboratory activities run in parallel with the theoretical lectures. Students will develop Python modules following standard open-source workflows based on GitHub. The laboratories progressively guide students from basic Python and Git usage to the implementation of simplified network-control functions, graph-based physical-layer abstraction, QoT-aware lightpath evaluation, routing and wavelength or spectrum assignment, and basic digital-twin operations. The course also includes seminars from companies, network operators, and organizations active in optical networking and open infrastructures, whenever available. These seminars are intended to connect the theoretical and laboratory contents with current industrial practice, standardization activities, open-source initiatives, and research challenges. The final assessment is based on the discussion of the assigned homework activities. Students will present and discuss the implemented software modules, the obtained results, and the underlying theoretical concepts. The homework path may also serve as a starting point for Master’s thesis projects carried out within the Telecom Infra Project and IOWN Global Forum frameworks, in collaboration with vendors and network operators, and experimentally validated on the open network infrastructure available in the Open PLANET Lab at Politecnico di Torino.

Studying material will be available on “portale della didattica”. Books to deepen specific topics will be suggested as well.

Lecture slides; Lab exercises; Video lectures (current year); Video lectures (previous years); Multimedia materials; Simulation tools;

Exam: Compulsory oral exam; Practical lab skills test;

The exam consists of an oral interview focused on the discussion of the homework assignments developed during the course. Students will be required to show, on their own laptop, the implemented solutions to the assigned exercises. The discussion will include the structure of the code, the adopted implementation choices, the obtained results, and the ability to run and explain the developed modules. Students will also be required to summarize their work and its theoretical implications through a PowerPoint presentation, or an equivalent presentation format. The quality of the presentation will be part of the evaluation, including the ability to organize the material clearly, effectively summarize the main results, and properly present figures, tables, plots, and other relevant outputs. The oral interview will also include theoretical questions related to the assignments and to the concepts discussed while commenting on the implemented work. These questions are intended to assess the student’s understanding of the theoretical foundations underlying the practical activities. Completion of the assignments is mandatory in order to pass the exam. The final mark is composed as follows: * 80% of the grade, corresponding to 24/30, is based on the presentation and discussion of the code, assignments, implementation choices, and results. * 20% of the grade, corresponding to 6/30, is based on the related theoretical questions. Up to 3 bonus points may be awarded for continuous and consistent development of the assignments during the course, as monitored through GitHub Classroom. In case of special needs, the exam procedure is fully compatible with online assessment.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.